Achieving real-time capability is an essential prerequisite for the
indu...
Microgels are cross-linked, colloidal polymer networks with great potent...
(Economic) nonlinear model predictive control ((e)NMPC) requires dynamic...
Meta-learning of numerical algorithms for a given task consist of the
da...
Graph neural networks (GNNs) are emerging in chemical engineering for th...
Ionic liquids (ILs) are important solvents for sustainable processes and...
Fuels with high-knock resistance enable modern spark-ignition engines to...
Electricity is traded on various markets with different time horizons an...
We present a specialized scenario generation method that utilizes foreca...
To model manifold data using normalizing flows, we propose to employ the...
The design and operation of modern energy systems are heavily influenced...
Neural networks-based learning of the distribution of non-dispatchable
r...
Diverse domains of science and engineering require and use mechanistic
m...
Gaussian processes (Kriging) are interpolating data-driven models that a...